Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Large language models (LLMs) have shown remarkable reasoning capabilities, especially
when prompted to generate intermediate reasoning steps (eg, Chain-of-Thought, CoT) …

World models, especially in autonomous driving, are trending and drawing extensive
attention due to their capacity for comprehending driving environments. The established …

We study how visual representations pre-trained on diverse human video data can enable
data-efficient learning of downstream robotic manipulation tasks. Concretely, we pre-train a …

A longstanding goal of the field of AI is a method for learning a highly capable, generalist
agent from diverse experience. In the subfields of vision and language, this was largely …

To solve tasks in complex environments, robots need to learn from experience. Deep
reinforcement learning is a common approach to robot learning but requires a large amount …